Outliers and Interquartile Range

A LevelAQAEdexcelOCR

Interquartile Range

Recall: The range is equal to the highest value subtract the lowest value.

The range is a measure of variation (how spread out the data is). It is affected severely by extreme values and outliers. To handle this problem, we introduce the interquartile range.

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Quartiles and the Interquartile Range

Quartiles are values that split the data into four, in the same way that the median splits the data into two (in fact, the median is the second quartile).

Recall: To find the median, we find n2dfrac{n}{2}, where nn is the frequency. If this is a whole number the median is the average of this term and the one above. If this is not a whole number we round the number up to find the position of the median term.

We find quartiles in very similar ways.

The first (or lower) quartile is calculated from n4dfrac{n}{4}. If this is a whole number then the first quartile is the average of this term and the term above. If this is not a whole number then we round the number up to find the position of the first quartile.

The third (or upper) quartile is calculated from 3n4dfrac{3n}{4}. If this is a whole number then the third quartile is the average of this term and the term above. If this is not a whole number then we round the number up to find the position of the third quartile.

Note: We always round up to find the position of the quartile, even if n4dfrac{n}{4} or 3n4dfrac{3n}{4} would usually be rounded down.

Finally we define the interquartile range:

interquartile range=third quartilefirst quartiletext{interquartile range}=text{third quartile}-text{first quartile}

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Outliers

The interquartile range provides a method to deal with outliers. Since it is not calculated using any outliers because it is the range of the middle half of the data, it is sensible to say that an outlier is a certain multiple of the interquartile range below the first quartile or above the third quartile. An exam question might, for example, provide a data set and ask you to calculate the interquartile range and find outliers.

 

Note: The particular multiplier you need to use to identify any outliers will be given to you in the question.

A LevelAQAEdexcelOCR
A LevelAQAEdexcelOCR

Example 1: The Interquartile Range

Consider the data set 1,4,5,5,6,6,6,6,7,10,121,4,5,5,6,6,6,6,7,10,12. What is the interquartile range?

[2 marks]

There are 1111 data points. 114=2.75dfrac{11}{4}=2.75, so the first quartile is in the third position, which is 55.

3×114=8.25dfrac{3times 11}{4}=8.25, so the third quartile is in the 99th position, which is 77. So the interquartile range is 75=27-5=2.

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Example 2: Outliers

Consider the data set 21,34,35,39,41,42,4421,34,35,39,41,42,44. A data point is said to be an outlier if it is more than 1.51.5 times the interquartile range above the third quartile or below the first quartile. Identify any outliers.

[5 marks]

There are 77 data points. 74=1.75dfrac{7}{4}=1.75, so the first quartile is in the second position, which is 3434.

3×74=5.25dfrac{3times 7}{4}=5.25, so the third quartile is in the 66th position, which is 4242. So the interquartile range is 4234=842-34=8.

Calculate boundaries for outliers: 1.5×8=121.5times 8=12 so the lower boundary is 3412=2234-12=22 and the upper boundary is 42+12=5642+12=56. The data value 2121 falls outside of these boundaries, so it is an outlier. There are no other outliers.

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Outliers and Interquartile Range Example Questions

Question 1: What is the interquartile range of this data set?

3,11,21,30,38,49,51,543,11,21,30,38,49,51,54

[2 marks]

A Level AQAEdexcelOCR

There are 88 data points. 84=2dfrac{8}{4}=2 so for the lower quartile we average the second and third points, which is 11+212=16dfrac{11+21}{2}=16. 3×84=6dfrac{3times 8}{4}=6 so for the upper quartile we average the sixth and seventh points, which is 49+512=50dfrac{49+51}{2}=50. So the interquartile range is 5016=3450-16=34.

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Question 2: Find the interquartile range of the data in this frequency table.

[4 marks]

A Level AQAEdexcelOCR

There are 100100 data points.

 

1004=25dfrac{100}{4}=25 so the first quartile is the average of the 2525th and 2626th data points, both of which are 33; so the first quartile is 33.

 

3×1004=75dfrac{3times 100}{4}=75 so the third quartile is the average of the 7575th and 7676th data points, both of which are 66; so the third quartile is 66.

Note: in which value a data point falls can be seen easily with a cumulative frequency table.

The interquartile range is 63=36-3=3

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Question 3: A value is said to be an outlier if it is more than 1.51.5 times the interquartile range above the third quartile or below the first quartile. How many outliers are there in the data set below?

 

4,16,36,44,46,48,48,49,49,49,50,52,54,55,56,58,63,72,81,994,16,36,44,46,48,48,49,49,49,50,52,54,55,56,58,63,72,81,99

 

[7 marks]

A Level AQAEdexcelOCR

There are 20 data points.

 

204=5dfrac{20}{4}=5 so the first quartile is the average of the fifth and sixth data point, which is 46+482=47dfrac{46+48}{2}=47

 

3×204=15dfrac{3times 20}{4}=15 so the third quartile is the average of the 1515th and 1616th data point, which is 56+582=57dfrac{56+58}{2}=57

 

The interquartile range is 5747=1057-47=10.

 

So our lower boundary is 471.5×10=3247-1.5times10=32 and our upper boundary is 56+1.5×10=7156+1.5times10=71.

 

44 and 1616 lie outside the lower boundary, while 7272, 8181 and 9999 lie outside the upper boundary, so there are five outliers overall.

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Additional Resources

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Exam Tips Cheat Sheet

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Formula Booklet

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Specification Points Covered

L3 – Interpret measures of central tendency and variation, extending to standard deviation
L4 – Recognise and interpret possible outliers in data sets and statistical diagrams, be able to clean data, including dealing with missing data, errors and outliers

Outliers and Interquartile Range Worksheet and Example Questions

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Outliers and Cleaning Data

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